In [37]:
def model_eval_bin(model_name,ytest,ypreds,yprobs2d,show_plots=True):
import sklearn.metrics as skmetrics
import scikitplot.metrics as skpmetrics
import os
acc = skmetrics.accuracy_score(ytest,ypreds)
precision = skmetrics.precision_score(ytest,ypreds)
recall = skmetrics.recall_score(ytest,ypreds)
f1 = skmetrics.f1_score(ytest,ypreds)
auc = skmetrics.roc_auc_score(ytest,ypreds)
print(skmetrics.classification_report(ytest,ypreds))
print(skmetrics.confusion_matrix(ytest,ypreds))
df_res = pd.DataFrame({'Accuracy':[acc],
'Precision': [precision],
'Recall': [recall],
'F1-score': [f1],
'AUC': [auc]},index=[model_name])
display(df_res.style.format("{:.4f}"))
if not os.path.isdir('../outputs'):
os.makedirs('../outputs')
o = '.' if ENV_COLAB else '../outputs/'
df_res.to_csv(o+f'model_{model_name}.csv',index=True)
skpmetrics.plot_precision_recall(ytest,yprobs2d) # more focus on minority
skpmetrics.plot_roc_curve(ytest,yprobs2d) # equal focus on both groups
skpmetrics.plot_confusion_matrix(ytest,ypreds)
model_eval_bin('LR',ytest,ypreds,yprobs2d,show_plots=True)
precision recall f1-score support
0 0.85 0.89 0.87 1035
1 0.65 0.55 0.60 374
accuracy 0.80 1409
macro avg 0.75 0.72 0.73 1409
weighted avg 0.79 0.80 0.80 1409
[[925 110]
[168 206]]
| Accuracy | Precision | Recall | F1-score | AUC | |
|---|---|---|---|---|---|
| LR | 0.8027 | 0.6519 | 0.5508 | 0.5971 | 0.7223 |
